Optimal One-Wafer Scheduling of Single-Arm Multi-Cluster Tools with Tree-Like Topology

ABSTRACT

The scheduling problem of a multi-cluster tool with a tree topology whose bottleneck tool is process-bound is investigated. A method for scheduling the multi-cluster tool to thereby generate an optimal one-wafer cyclic schedule for this multi-cluster tool is provided. A Petri net (PN) model is developed for the multi-cluster tool by explicitly modeling robot waiting times such that a schedule is determined by setting the robot waiting times. Based on the PN model, sufficient and necessary conditions under which a one-wafer cyclic schedule exists are derived and it is shown that an optimal one-wafer cyclic schedule can be always found. Then, efficient algorithms are given to find the optimal cycle time and its optimal schedule. Examples are used to demonstrate the scheduling method.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Patent Application No. 62/221,035, filed on Sep. 20, 2015, which is incorporated by reference herein in its entirety.

LIST OF ABBREVIATIONS

BM buffer module

CTC cluster-tool-chain

FP fundamental period

LL loadlock

PM process module

PN Petri net

R robot

ROPN resource-oriented PN

BACKGROUND Field of the Invention

The present invention generally relates to scheduling a tree-like multi-cluster tool. In particular, the present invention relates to a method for scheduling this multi-cluster tool to thereby generate an optimal one-wafer cyclic schedule.

LIST OF REFERENCES

There follows a list of references that are occasionally cited in the specification. Each of the disclosures of these references is incorporated by reference herein in its entirety.

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There follows a list of patent(s) and patent application(s) that are occasionally cited in the specification.

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Description of Related Art

With single wafer processing technology, cluster tools are increasingly used in the semiconductor industry. Generally, a single-cluster tool is composed of four to six process modules (PM), one wafer-delivering robot (R), and two loadlocks (LL) for cassette loading/unloading. The robot can be single or dual-arm one resulting in single or dual-arm cluster tools. Several single-cluster tools are connected together by shared buffer modules (BMs) to form a more integrated manufacturing system called a multi-cluster tool. It has higher performance and becomes more and more popular in the semiconductor industry. However, it is very challenging to effectively operate such a system.

Extensive studies have been done on modeling, performance evaluation, and scheduling for single-cluster tools [Kim et al., 2003; Lee et al., 2004; Lee and Park, 2005; Lopez and Wood, 2003; Wu et al., 2008a; Wu and Zhou, 2010b; Qiao et al., 2012a and 2012b; and Wu et al., 2013a and 2013b]. It is found that a cluster tool may operate in process-bound or transport-bound region. For a single-cluster tool, if one of its process steps is the bottleneck of the tool in the sense of workload, it is process-bound. In this case, the cycle time of the system is determined by the wafer processing time. Otherwise, if the robot is the bottleneck, it is transport-bound and the cycle time is determined by the robot task time. In practice, the robot task time is much shorter than the wafer processing time [Kim et al., 2003; and Lopez and Wood, 2003] such that, for a single-arm cluster tool, a backward strategy is shown to be optimal [Lee et al., 2004; Dawande 2002; and Lopez and Wood, 2003]. Some wafer fabrication processes require that a processed wafer should leave a PM within a given time interval, which is called wafer residency time constraints [Kim et al., 2003; Lee and Park, 2005]. Without immediate buffer between the PMs in a tool, this greatly complicates the problem of scheduling single-cluster tools. This problem is studied by using Petri nets and mathematical programming models for dual-arm cluster tools to find an optimal periodic schedule in [Kim et al., 2003; Lee and Park, 2005]. It is further studied for both single and dual-arm cluster tools in [Wu et al., 2008; Wu and Zhou, 2010b; and Qiao et al., 2012a and 2012b] by developing generic Petri net (PN) models. With these models, robot waiting is explicitly modeled as an event to parameterize a schedule by robot waiting time. Then, to find a schedule is to determine the robot waiting time. With these models, schedulability conditions are presented and closed-form scheduling algorithms are given to find an optimal periodic schedule if schedulable.

In recent years, attention has been paid to the problem of scheduling multi-cluster tools. A multi-cluster tool composed of K single-cluster tools is called a K-cluster tool. A heuristic method is proposed in [Jevtic, 2001] for it by dynamically assigning priorities to PMs. However, the performance of such a schedule is difficult to evaluate. Geismar et al. examine a serial 3-cluster tool composed of single-arm tools and parallel processing modules. By simulation, they find that, for 87% of instances, the backward strategy achieves the lower-bound of cycle time. In [Ding et al., 2006], an event graph model is used to describe the dynamic behavior of the system and a simulation-based search method is proposed to find a periodic schedule.

To reduce the computational complexity, without considering the robot moving time, a decomposition method is proposed in [Yi et al., 2005 and 2008]. By this method, the fundamental period (FP) for each tool is calculated as done for scheduling single-cluster tools. Then, by analyzing time delays resulting from accessing the shared buffers, the global fundamental period, or the cycle time for the system is determined. In this way, a schedule is found.

With robot moving time considered, a polynomial algorithm is presented to find a multi-wafer schedule for a serial multi-cluster tool in [Chan et al., 2011a]. A K-cluster tool is said to be process-dominant if its bottleneck tool is process-bound. It is known that there is always an optimal one-wafer schedule for a process-dominant serial multi-cluster tool [Zhu et al., 2013a, 2012; and Zhu et al., 2013b]. In studying the effect of buffer spaces in BMs on productivity, Yang et al. [2014] show that, for a process-dominant serial multi-cluster tool with two-space BMs, there is a one-wafer cyclic schedule such that the lower bound of cycle time is reached. For a single-arm multi-cluster tool with two-space BMs and wafer residency time constraints, Liu and Zhou [2013] propose a non-linear programming model and a heuristic algorithm to solve it. For a single-arm multi-cluster tool with single-space BMs and wafer residency time constraints, Zhu et al. [2014 and 2015] present sufficient and necessary schedulability conditions and an efficient algorithm to find a one-wafer optimal cyclic schedule if schedulable.

Chan et al. [2011b] study the problem of scheduling a tree-like multi-cluster tool, which is the only report on tree-like multi-cluster tool scheduling to our best knowledge. The obtained schedule is a multi-wafer cyclic one. It derives conditions under which a tree-like multi-cluster tool can be scheduled by a decomposition method. Further, if decomposable, conditions under which a backward scheduling strategy is optimal are presented.

Since a one-wafer cyclic schedule is easy to implement and understand by a practitioner, it gives rise to a question if there exists a one-wafer cyclic schedule for a tree-like multi-cluster tool and how such a schedule can be found if it exists. There is a need in the art to derive the conditions for which this schedule exists and to develop a method for scheduling a tree-like multi-cluster tool with a one-wafer cyclic schedule.

SUMMARY OF THE INVENTION

An aspect of the present invention is to provide a computer-implemented method for scheduling a tree-like multi-cluster tool to generate a one-wafer cyclic schedule. The multi-cluster tool comprises plural single-cluster tools each having a robot for wafer handling, and is decomposable into a plurality of CTCs each having a serial topology.

The method comprises determining robot waiting times for the robots. In particular, the method makes use of Theorem 1 detailed below to determine the robot waiting times.

Preferably, a CTC-Check algorithm as detailed hereinafter computes candidate values of the robot waiting times for at least one individual CTC. The CTC-Check algorithm is configured to compute these candidate values such that the robot waiting time at a last processing step of each single-cluster tool in the individual CTC apart from the leaf tool therein is minimized.

With the use of the CTC-Check algorithm, the robot waiting times may be determined according to Algorithms 1 and 2 detailed below.

Other aspects of the present invention are disclosed as illustrated by the embodiments hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a tree-like multi-cluster tool.

FIG. 2 depicts a PN model for an individual cluster tool C_(i) as an illustrative example.

FIG. 3 depicts a PN model for a BM shared by adjacent clusters C_(i) and C_(a) as an illustrative example.

FIG. 4 shows a multi-cluster tool considered for analysis in Example 1.

DETAILED DESCRIPTION

Since a one-wafer cyclic schedule is easy to implement and understand by a practitioner, it gives rise to a question if there exists a one-wafer cyclic schedule for a tree-like multi-cluster tool and how such a schedule can be found if it exists. The present work provides a solution of schedulability and a method for scheduling the tree-like multi-cluster tool with the one-wafer cyclic schedule. Since the robot task time is much shorter than the wafer processing time in practice [Kim et al., 2003; and Lopez and Wood, 2003], it is reasonable to assume that the bottleneck tool of a multi-cluster tool is process-bound, or it is process-dominant. Hence, we address a process-dominant tree-like multi-cluster tool in this work. The key for scheduling a K-cluster tool is to coordinate the activities of the multiple robots in accessing the BMs. In scheduling single-cluster tools, Wu et al. [2008] and Wu and Zhou [2010a] find that it is crucial to determine when a robot should wait and how long it should wait if necessary. By following this idea, the present work develops a PN model for a tree-like multi-cluster tool with the robot waiting being explicitly modeled. With the model, a schedule for the system is parameterized by the robot waiting time. For a process-dominant tree-like multi-cluster tool, necessary and sufficient conditions under which a one-wafer cyclic schedule exists are derived. It is then shown that there is always a one-wafer cyclic schedule. An efficient algorithm is developed and disclosed herein to find the minimal cycle time and the one-wafer cyclic schedule.

A. SYSTEM MODELING BY PETRI NET A.1. Tree-Like Multi-Cluster Tool

The configuration of a tree-like multi-cluster tool is illustrated in FIG. 1. A tree-like multi-cluster tool composed of K single-cluster tools is called a K-tree-cluster tool. It follows from Chan et al. [2011b] that a K-tree-cluster tool can be described as follows. Let N_(K)={1, 2, . . . , K} be the index set of the cluster tools that form a K-tree-cluster tool. The tool with the LLs in it is called the “root” and is denoted by C₁. If C_(i), i∈N_(K)\{1}, has only one adjacent tool, it is called a leaf tool. From C₁ to each leaf, there is a directed path. On such a path, if C_(i) is closer to C₁ than C_(j), C_(i) is the upstream tool of C_(j).

As done in [Chan et al. 2011b], for a K-tree-cluster tool, we assume: 1) the root has two LLs; 2) a BM shared by two adjacent tools can hold one wafer at a time without a processing function; 3) a PM can process one wafer at a time; 4) all the wafers in a cassette should be processed in an identical sequence specified in the recipe and visit a PM no more than once (except for a BM); 5) the loading, unloading, moving time of the robots, and the processing time of a wafer at a PM are deterministic and known; 6) the loading and unloading time of a robot is same, and the robot moving time from a PM to another is also same regardless of whether it carries a wafer or not; and 7) there is no parallel module, or only one PM is configured for each processing step.

With two LLs, while one lot of wafers in one LL is being processed, the other LL can be used for loading/unloading another lot of wafers. In this way, a multi-cluster tool can work consecutively without interruption. Thus, it operates in a steady state at most of time and there are always wafers to be processed. This is equivalent to that the LLs have infinite capacity.

A.2. Operation Process

The wafer fabrication process can be illustrated by the 5-tree-cluster tool shown in FIG. 1. With the LL and BMs being treated as a step, let PS_(ij) denote the j-th step in C_(i). Then, the processing route of a wafer is: PS₁₀ (LL)→PS₁₁→PS₁₂ (PS₂₀)→PS₂₁→PS₂₂→PS₂₃→PS₂₄→PS₂₀ (PS₁₂)→PS₁₃→(PS₃₀)→PS₃₁→PS₃₂ (PS₄₀)→PS₄₁→PS₄₂ (PS₅₀)→PS₅₁→PS₅₂→PS₅₃→PS₅₄→PS₅₀ (PS₄₂)→PS₄₃→PS₄₄→PS₄₀ (PS₃₂)→PS₃₃→PS₃₄→PS₃₀ (PS₁₃)→PS₁₄→PS₁₀ (LL).

In C_(i), i∈N_(K)\{1}, the BM denoted as PM_(i0) can be viewed as a virtual LL for injecting wafers from an upstream tool, while the other BM(s) PM_(ij) (j>0) can be viewed as a virtual process module(s). The BM shared by adjacent tools C_(i) and C_(j) is called an outgoing buffer for C_(i) and an incoming one for C_(j). The LLs in C₁ can be treated as an incoming buffer with infinite capacity. There are a number of processing steps in each individual tool and a BM connecting two tools is treated as a processing step with processing time being zero. Let Ψ denote the index set of leaf tools and DI(C_(i)) the index set of the immediate downstream tools of C_(i) (i∉Ψ). We have DI(C_(i))=Ø if i∈Ψ. Further, let PM_(i(b[i,k])) denote the BM shared by C_(i) and C_(k), k∈DI(C_(i)), with PM_(k0) being the incoming buffer or virtual LL for C_(k). It is called Step b[i,k] for C_(i) and Step 0 for C_(k), respectively. Assume that, except the incoming buffer, there are n[i] steps in C_(i). Then, there are n[i]+1 steps in C_(i) denoted as PS_(i0), PS_(i1), . . . , PS_(i(n[i])) with PS_(i0) and PS_(i(b[i,k])) being the incoming and outgoing steps. Let R_(i) denote the robot in C_(i). As discussed above, at most of time, a multi-cluster tool operates in a steady state. Under such a state, for a process-dominant multi-cluster tool, a backward strategy [Dawande 2002] is optimal for each individual tool.

A.3. Modeling the Wafer Flow

We model a multi-cluster tool system by extending the resource-oriented PN (ROPN) proposed in [Wu, 1999; Wu et al., 2008a and 2008b; and Wu and Zhou, 2009]. It is a finite capacity PN=(P, T, I, O, M, K), where P is a finite set of places; T is a finite set of transitions, P∪T≠Ø, and P∩T=Ø; I:P×T→N={0, 1, 2, . . . } is an input function; O:P×T→N is an output function; M:P→N is a marking denoting the number of tokens in P with M₀ being the initial marking; and K:P→N\{0} is a capacity function with K(p) being the number of tokens that p can hold at a time. The basic concept, transition enabling and firing rules of Petri nets (PN) can be found in [Zhou et al., 1998; Wu and Zhou, 2009].

To model a multi-cluster tool, one needs to model the individual tools and the BMs. The behavior of Step j in C_(i) is modeled as follows. The PM for Step j in C_(i) with i≠1 and j≠0 is modeled by timed place p_(ij) with K(p_(ij))=1. Place p₁₀ models the LLs in C₁ with K(p₁₀)=∞. Places z_(ij) and d_(ij) model R_(i)'s holding a wafer (token) for loading into p_(ij) and moving from Steps j to j+1 (or Step 0 if j=n[i]), respectively. Place q_(ij) models R_(i)'s waiting at Step j for unloading a wafer there. Transitions t_(ij) and u_(ij) model R_(i)'s loading and unloading a wafer into and from PM_(ij), respectively. Then, with arcs (z_(ij), t_(ij)), (t_(ij), p_(ij)), (p_(ij), u_(ij)), (q_(ij), u_(ij)), and (u_(ij), d_(ij)), of the PN model for Step j in C_(i) is obtained as shown in FIG. 2.

With the model for a step as a module, tool C_(i) is modeled as follows. Place r_(i) models R_(i) with K(r_(i))=1, meaning that, with only one arm, the robot can hold one wafer at a time. Timed y_(ij) with arcs (r_(i), y_(ij)) and (y_(ij), q_(ij)) for connecting r_(i) and q_(ij), representing that R_(i) moves from Steps j+2 to j, j=0, 1, . . . , n[i]−2 (y_(i(n[i]−1)) for moving from Steps 0 to n[i]−1, and y_(i(n[i])) for moving from Steps 1 to n[i]). Finally, x_(ij), j=0, 1, 2, . . . , n[i]−1, are added between d_(ij) and z_(i(j+1)) with arcs (d_(ij), x_(ij)) and (x_(ij), z_(i(j+1))); and x_(i(n[i])) is added between d_(i(n[i])) and z_(i0) with arcs (d_(i(n[i])), x_(i(n[i]))) and (x_(i(n[i])), z_(i0)). In this way, the modeling of C_(i) is completed as shown in FIG. 2.

A BM linking C_(i) and C_(a), a∈DI(C_(i)), is denoted by PM_(i(b[i,a])) in C_(i) and PM_(a0) in C_(a). Thus, p_(i(b[i,a])) and p_(a0) with p_(i(b[i,a]))=p_(a0) are used to model the BM. Then, Step b[i,a] for C_(i) is modeled by {p_(i(b[i,a])), q_(i(b[i,a])), z_(i(b[i,a])), d_(i(b[i,a])), t_(i(b[i,a])), u_(i(b[i,a]))} and Step 0 for C_(a) by {P_(a0), q_(a0), z_(a0), d_(a0), t_(a0), u_(a0)}, respectively. Thereafter, when we refer to Step b[i,a] in C_(i), we use p_(i(b[i,a])), whereas for Step 0 in C_(a) we use p_(a0). It should be pointed out that Step 0 in C₁ (the LLs) is not shared with any other tool and there is no outgoing buffer in a leaf tool C_(L). The PN model for the BM linking C_(i) and C_(a) is shown in FIG. 3.

With the PN structure developed, marking M₀ is set as follows. To describe the idle state of the system, a special type of token called V-token representing a virtual wafer is introduced. As mentioned above, there is a process-bound bottleneck tool in the system. Hence, in the steady state, if there is a wafer being processed in every PM_(ij) with j≠0, the system reaches its maximal throughput. Let χ denote the index set of non-leaf tools N_(K)=χ∪Ψ with χ∩Ψ=Ø. Then, M₀ is set as follows. For C_(i), i∈N_(K), set M₀(p_(ij))=1 for j≠1 with a V-token, M₀(p_(ij))=0, j=1, and M₀(p₁₀)=n; M₀(q_(ij))=M₀(z_(ij))=M₀(d_(ij))=0 for any j; and M₀(r_(i))=1. It is assumed that the token in p_(i(b[i,a])) enables u_(a0), where a∈DI(C_(i)).

It is easy to show that the obtained PN is deadlock-prone. To make it deadlock-free, let Ω_(n)=N_(n)∪{0}, a control policy on y_(ij)'s is defined as follows.

Definition 1: At marking M, y_(ij), i∈N_(K) and j∈Ω_(n[i]−1), is said to be control-enabled if M(p_(i(j+1)))=0; y_(1(n[i])) is control-enabled if M(p_(1(n[i])))=1; and y_(i(n[i])), i∈N_(K)\{1}, is control-enabled if M(p_(i0))=0.

With Definition 1, the PN for an individual tool is shown to be deadlock-free [Wu et al., 2008a]. Thereafter, it is assumed that the PN is controlled by the policy given in Definition 1.

In the PN model for a BM shared by C_(i) and C_(a) shown in FIG. 3, p_(i(b[i,a])) (p_(a0)) has two output transitions u_(i(b[i,a])) and u_(a0), leading to a conflict. A token entering p_(i(b[i,a])) by firing t_(i(b[i,a])) in C_(i) should enable u_(a0) in C_(a), while a token entering p_(a0) by firing t_(a0) in C_(a) should enable u_(i(b[i,a])) in C_(i). Colors are introduced to make the model conflict-free as follows.

Definition 2: Define the unique color of transition t_(i) as C(t_(i))={c_(i)}.

By Definition 2, the colors for u_(i(b[i,a])) and u_(a0) are c_(i(b[i,a])) and c_(a0), respectively. Then, the color for a token in p_(i(b[i,a])) or p_(a0) is defined as follows.

Definition 3: For i∈χ, a∈DI(C_(i)), a token entering p_(i(b[i,a])) by firing t_(i(b[i,a])) in C_(i) has color c_(a0), while a token entering p_(a0) by firing t_(a0) in C_(a) has color c_(i(b[i,a])).

It is easy to check that, by Definitions 2 and 3, the model is conflict-free. The obtained model can also describe the behavior of initial and final transient processes as explained as follows. At M₀, y_(i0) is enabled and fires leading to M₁(q_(i0))=1. By starting from M₁, transition u₁₀ fires and W₁ representing the first wafer is unloaded from p₁₀. In this way, every time when u₁₀ fires, a token for a real wafer is delivered into p₁₁. With the transition enabling and firing rules, the model evolves to reach a marking M such that every V-token is removed from the model and is replaced by a token for a real wafer, or a steady state is reached. The evolution from M₀ to M presents the initial transient process. Similarly, when the system needs to stop working, every time when u₁₀ fires, a V-token is delivered into p₁₁, the model can evolve back to marking M₀, which describes the final transient process.

A.4. Modeling Activity Time

To describe the temporal aspect, time is associated with both transitions and places. It follows from Kim et al. [2003] and Lee and Lee [2006] that the time for the robot to move from one step to another is a constant, and so is the time for the robot to load/unload a wafer into/from a PM. The symbols and time durations for places and transitions are listed in TABLE 1.

TABLE 1 The time durations associated with transitions and places (i∈N_(n), j∈Ω_(n[i])). Transition or Time Symbol place Actions required λ_(i) u_(ij)∈T Robot R_(i) unloads a wafer from p_(ij) Constant λ_(i) t_(ij)∈T Robot R_(i) loads a wafer into p_(ij) μ_(i) y_(ij)∈T Robot R_(i) moves from a step to Constant μ_(i) another x_(ij)∈T α_(ij) p_(ij)∈P Wafer processing in p_(ij) Constant α_(ij) τ_(ij) p_(ij)∈P Wafter sojourn in p_(ij) τ_(ij) ω_(ij) q_(ij)∈P Robot R_(i) waits before unloading a [0, ∞) wafer from p_(ij) z_(ij), d_(ij)∈P No time is associated 0

B. PROPERTIES OF SCHEDULING A K-CLUSTER TOOL

This section recalls the properties of scheduling an individual tool in [Zhu et al., 2013]. For a process-dominant K-tree-cluster tool, since a backward strategy is optimal for each individual tool, with the PN model shown in FIG. 2, R_(i)'s cycle time is given by [Wu et al., 2008]

ψ_(i)=2(n[i]+1)(λ_(i)+μ_(i))+Σ_(j=0) ^(n[i])ψ_(ij)=ψ_(i1)+ψ_(i2) , i∈N _(K),  (1)

where ψ_(i1)=2(n[i]+1)(λ_(i)+μ_(i)) is R_(i)'s task time and is a constant, and ψ_(i2)=Σ_(j=0) ^(n[i])ω_(ij) is the robot waiting time in a cycle.

With robot waiting as an event, the time taken for completing a wafer at Step j in C_(i), i∈N_(K), is given by [Wu et al., 2008]

ξ_(ij)=α_(ij)+4λ_(i)+3μ_(i)+ω_(i(j−1)) , i∈N _(K) and j∈N _(n[i]),  (2)

and

ξ_(i0)=4λ_(i)+3μ_(i)+ω_(i(n[i])) , i∈N _(K).  (3)

Note that, since a BM has no processing function, we have α_(i0)=α_(i(b[i,a]))=0, α∈DI(C_(i)). With α_(ij), λ_(i), and μ_(i) being constants and ω_(ij)'s being variable, if ω_(ij) is set to be zero, the shortest time taken for completing a wafer at Step j in C_(i) is

η_(ij)=α_(ij)+4λ_(i)+3μ_(i) , i∈N _(K) and j∈Ω _(n[i]).  (4)

A cluster tool may be scheduled such that a wafer stays in PM_(ij) for some time after its completion, or τ_(ij)≧α_(ij), where τ_(ij) is the wafer sojourn time in PM_(ij). For Step j∈{0, b[i,a]}, a∈DI(C_(i)), or a BM, the wafer unloaded from PS_(ij) by the ƒ-th firing of u_(ij) is not the one that is loaded into PS_(ij) by the ƒ-th firing of t_(ij). However, for scheduling purpose, we concern only the time needed for completing the tasks in accessing a BM but not if they are the same wafer. Hence, the virtual wafer sojourn time for Step j∈{0, b[i,a]}, a∈DI(C_(i)), is introduced as follows. Let υ₁ and υ₂ be the end time point of the k-th firing of t_(ij) and the start time point of the k-th firing of u_(ij), respectively. The virtual wafer sojourn time is defined as τ_(ij)=υ₂−υ₁. In this way, for all steps in C_(i), τ_(ij) has the same meaning. Then, by replacing π_(ij) with τ_(ij) in (2) and (3), the cycle time at Step j in C_(i) is given by

θ_(ij)=τ_(ij)+4λ_(i)+3μ_(i)+ω_(i(j−1)) , i∈N _(K) and j∈N _(n[i]),  (5)

and

θ_(i0)=τ_(i0)+4λ_(i)+3μ_(i)+ω_(i(n[i])) , i∈N _(K).  (6)

Let Π_(i)=max{η_(i0), η_(i1), . . . , η_(i(n[i])), ψ_(i1)}. If Π_(i)=ψ_(i1), C_(i) is transport-bound, otherwise it is process-bound. The manufacturing process in C_(i) is a serial one and C_(i) should be scheduled such that

Θ_(i)=θ_(i0)=Θ_(i1)= . . . =Θ_(i(n[i])) and τ_(ij)≧α_(ij) , i∈N _(K) and j∈Ω _(n[i])  (7)

where Θ_(i) is the cycle time of C_(i). To find a feasible schedule, Θ_(i)≧Π_(i) must hold. Given Θ_(i) for C_(i), R_(i) should be scheduled such that ψ_(i)=Θ_(i). Thus, ψ_(i2)=Θ_(i)−ψ_(i1) is the R_(i)'s waiting time in a cycle, or the schedule is a function of R_(i)'s waiting time. This implies that a one-wafer cyclic schedule for a multi-cluster tool is determined by the robots' waiting time. The key here is how to determine the minimal Θ_(i)'s.

Let Θ denote the cycle time of a K-tree-cluster tool and Θ_(i) the cycle time of C_(i), i∈N_(K). From [Zhu et al., 2013b], it can easily show that, to obtain a one-wafer cycle schedule for a K-tree-cluster tool, Θ₁=Θ₂= . . . =Θ_(K)=Θ should hold. Let C_(h), 1≦h≦K, be the bottleneck tool in a K-tree-cluster tool and Π_(h) be its FP. Then, Θ₁=Θ₂= . . . =Θ_(K)=Θ≧Π_(h) must hold. Hence, to obtain a one-wafer cycle schedule for a K-tree-cluster tool, every individual tool should have the same cycle time and, at the same time, the multiple robots should be coordinated such that all the individual tools operate in a paced way.

Assume that C_(i), ∀i∈N_(K). It is scheduled to have a cycle time Θ. Examine C_(i) and C_(a), i∉Ψ and a∈DI(C_(i)). If the system is scheduled such that, every time after R_(i) loads a wafer into PS_(i(b[i,a])) by firing t_(i(b[i,a])), u_(a0) fires immediately to unload this wafer by R_(a), then C_(i) and C_(a) can operate in a paced way. If a K-tree-cluster tool is scheduled such that, any pair of C_(i) and C_(a) can operate in such a way, a one-wafer cyclic schedule is obtained. We then examine how C_(i) and C_(a), i∉Ψ and a∈DI(C_(i)), can be scheduled to operate in a paced way. Let φ_(i0) be the start time point for firing u_(i0) in C_(i). Between the firings of u_(i0) and t_(i(b[i,a])), the following transition firing sequence is executed: σ_(i)=

firing u_(i0) (with time λ_(i))→x_(i0)(μ_(i))→t_(i1)(λ_(i))→y_(i(n[i]))(μ_(i))→robot waiting in q_(i(n[i]))(ω_(i(n[i])))→u_(i(n[i]))(λ_(i))→x_(i(n[i]))(μ_(i))→t_(i0)(λ_(i))→ . . . →y_(i(b[i,a]−1))(μ_(i))→robot waiting in q_(i(b[i,a]−1))(ω_(i(b[i,a]−1)))→u_(i(b[i,a]−1))(λ_(i))→x_(i(b[i,a]−1))(μ_(i))→t_(i(b[i,a]))(λ_(i))

. Given a schedule for C_(i) by setting ω_(ij)'s, the time taken by σ_(i) is Δ_(i)={(n[i]+1)−(b[i,a]−2)}(2λ_(i)+2μ_(i))−μ_(i)+Σ_(j=b[i,a]−1) ^(n[i])ω_(ij), if b[i,a]>1; and Δ_(i)=2λ_(i)+μ_(i), if b[i,a]=1. Then, u_(a0) starts to fire at φ_(a0)=φ_(i0)+Δ_(i), i∉Ψ. In other words, to make C_(i) and C_(a) operate in a paced way with a cycle time Θ, u_(a0) should fire Δ_(i) time units later than the firing of u_(i0).

With the PN model, the above requirement can be implemented by properly scheduling C_(i) and C_(a) as follows. At M₀ that is set as above discussed, u₁₀ fires and W₁ representing the first real wafer released into the system is unloaded from p₁₀. Then, for an m ∈DI(C₁), transition firing sequence σ₁=

firing u₁₀(λ₁)→x₁₀(μ₁)→t₁₁(λ₁)→y_(1(n[1]))(μ₁)→robot waiting in q_(1(n[1]))(ω_(1(n[1])))→u_(1(n[i]))(λ₁)→x_(1(n[1]))(μ₁)→t₁₀(λ₁)→ . . . →y_(1(b[1,m]−1))(μ₁)→robot waiting in q_(1(b[1,m]−1))(ω_(1(b[1,m]−1)))→u_(1(b[1,m]−1))(λ₁)→x_(1(b[1,m]−1))(μ₁)→t_(1(b[1,m]))(λ₁)

is executed in C₁. By firing t₁₁, W₁ is loaded into PS₁₁. After firing t_(1(b[1,m])), u_(m0) is enabled and fires. The time taken by σ₁ is Δ₁={(n[1]+1)−(b[1, m]−2)}(2λ₁+2μ₁)−μ₁+Σ_(j=b[1,m]−1) ^(n[1])ω_(ij) such that φ_(m0)=φ₁₀+Δ₁. Then, in C₁, t_(1(b[1,m])) fires again after a cycle. Meanwhile, in C_(m), a cycle is also completed. Since the cycle time is Θ for both C₁ and C_(m), u_(m0) fires again just after t_(1(b[1,m])) fires. During this cycle, W₂ is released into the system.

By starting from the firing of u_(m0), transition firing sequence σ_(m)=

u_(m0)(λ_(m))→x_(m0)(μ_(m))→t_(m1)(λ_(m))→y_(m(n[m]))(μ_(m))→robot waiting in q_(m(n[m]))(ω_(m(n[m])))→u_(m(n[m]))(λ_(m))→x_(m(n[m]))(μ_(m))→t_(m0)(λ_(m))→ . . . →y_(m(b[m,d]−1))(μ_(m))→robot waiting in q_(m(b[m,d]−1))(ω_(m(b[m,d]−1)))→u_(m(b[m,d]−1))(λ_(m))→x_(m(b[m,d]−1))(μ_(m))→t_(m(b[m,d]))(λ_(m))

is executed in C_(m), d∈DI(C_(m)). After firing t_(m(b[m,d])), u_(d0) is enabled and fires. The time taken by σ_(m) is Δ_(m) such that φ_(d0)=φ_(m0)+Δ_(m). This process can be performed for every pair of C_(m) and C_(d), d∈DI(C_(m)), such that every tool in the system acts in a paced way. Notice that, after each cycle in C₁, a V-token is removed from the system. When all V-tokens are removed, the steady state is reached and the system operates according to the backward one-wafer cycle schedule.

Notice that, to perform the above process for C_(m) and C_(d), d∈DI(C_(m)), it requires that: 1) when R_(m) arrives at p_(m(b[m,d])) for firing u_(m(b[m,d])) (t_(m(b[m,d]))), there is a token in p_(m(b[m,d])) (p_(m(b[m,d])) is emptied); and 2) when R_(d) arrives at p_(d0) for firing u_(d0) (t_(d0)), there is a token in p_(d0) (p_(d0) is emptied). To make these requirements satisfied, we have the following theorem.

Theorem 1: Given Θ≧Π_(h) as cycle time for a process-dominant K-tree-cluster tool, a one-wafer cyclic schedule exists if and only if, for any pair of C_(i) and C_(a), a∈DI(C_(i)), the following conditions are satisfied by determining ω_(ij)'s and ω_(aƒ)'s:

θ_(ij)=θ_(aƒ) =Θ,j∈Ω _(n[i]) and ƒ∈Ω_(n[a]),  (8)

τ_(i(b[i,a]))≧4λ_(a)+3μ_(a)+ω_(a(n[a]))  (9)

and

τ_(a0)≧4λ_(i)+3μ_(i)+ω_(i(b[i,a]−1)).  (10)

The proof of Theorem 1 is similar to that in [Zhu et al., 2013b] and omitted. By Theorem 1, to find a one-wafer cyclic schedule, the key is to determine Θ such that (8)-(10) are satisfied by setting ω_(ij)'s and ω_(aƒ)'s. This issue is discussed next.

C. ONE-WAFER CYCLIC SCHEDULING C.1. Scheduling of a Cluster-Tool-Chain

By viewing an individual tool as a vertex, the topology of a K-tree-cluster tool is a tree (directed graph) [Cormen et al., 2001]. Let χ(i,j) denote the index set of the tools on the path from C_(i) to C_(j), including i and j, and |χ(i,j)| be its cardinality. The length of the path from C_(i) to C_(j) is defined as the number of arcs connecting two adjacent tools on a path and is denoted by Len(C_(i), C_(j))=|χ_((i,j))|−1. If there is a path from C_(i) to C_(j) and Len(C₁, C_(i))<Len(C₁, C_(j)), C_(i) is an upstream tool of C_(j) and C_(j) is a downstream one of C_(i). Let O-Deg(C_(i)) denote the number of downstream tools immediately adjacent to C_(i). C_(i) is called a fork tool if O-Deg(C_(i))≧2. For example, in the 5-tree-cluster tool shown in FIG. 1, Len(C₁, C₅)=3, O-Deg(C₁)=2, and O-Deg(C₃)=O-Deg(C₄)=1.

A multi-cluster tool with serial topology can be seen as a cluster-tool-chain (CTC). Then, a K-tree-cluster tool can also be seen to be formed by a number of CTCs. A CTC that starts from C_(ƒ) and ends at C_(l) is denoted as CTC[ƒ,l]. Note that a CTC can be formed by a single tool and in this case we have ƒ=l. Let UI(C_(i)) denote the index of the immediate upstream tool of C_(i). Since C₁ has no upstream tool, we set UI(C₁)=0. Then, we divide a K-tree-cluster tool into a number of CTCs such that, for a CTC[ƒ,l], we have O-Deg(C_(j))≧2, j=UI(C_(ƒ)) and ƒ≠1; either O-Deg(C_(l))≧2 or O-Deg(C_(l))=0; and O-Deg(C_(l))=1, i∈χ_((i,j)), i≠ƒ, and i≠l. For a K-tree-cluster tool, the individual tools can be labeled such that the tools on each CTC[ƒ,l] are labeled with consecutive numbers in an increasing order from ƒ to l. That is, the immediate upstream and downstream tools of C_(i) are C_(i−1) and C_(i+1), respectively. For example, the 5-tree-cluster tool shown in FIG. 1 has three CTCs: {C₂}, {C₃, C₄, C₅}, and {C₁}. Algorithm 2 is presented later to find such CTCs. Before presenting the method for scheduling a K-tree-cluster tool, we discuss how to schedule the CTCs first.

Given CTC[ƒ,l], there is only one BM shared by C_(i) and C_(i+1), i∈N_([ƒ,l−1])={ƒ, ƒ+1, . . . , l−1}. Hence, for the simplicity of presentation, for a CTC, we can use PS_(i(b[i])) for the BM in C_(i) instead of PS_(i(b[i,i+1])). Let φ_(i(b[i]))=4λ_(i)+3μ_(i)+ω_(i(b[i]−1)) and φ_(i0)=4λ_(i)+3μ_(i)+ω_(i(n[i])). Examining the conditions given in Theorem 1, one can find that, to schedule CTC[ƒ,l] from C_(l) to C_(ƒ) such that these conditions are satisfied, one needs to set the robots' waiting time such that: 1) ω_(l(n[l])) is as small as possible; and 2) ω_(i(n[i])) and ω_(i(b[i]−1)), i∈N_([ƒ,l−1]), are as small as possible. Given cycle time Θ≧Π_(h), this can be implemented as follows: (1) set ω_(ij)=min{Θ−(4λ_(l)+3μ_(l)+α_(l(j+1))), Θ−2(n[l]+1)(λ_(l)+μ_(l))−Σ_(m∈Ω) _(j) _(\{j})ω_(lm)}; j∈Ω_(n[l]−1); (2) ω_(l(n[l]))=Θ−2(n[l]+1)(λ_(l)+μ_(l))−Σ_(m=0) ^(n[l]−1)ω_(lm); (3) τ_(l0)=Θ=φ_(l0); and (4) set ω_(ij)'s from i=l−1 to ƒ according to

ω_(i(b[i]−1))=min{τ_((i+1)0)−(4λ_(i)+3μ_(i)),Θ−2(n[i]+1)(λ_(i)+μ_(i))}  (11)

ω_(ij)=min{Θ−(4λ_(i)+3μ_(i)+α_(i(j+1))),Θ₀−2(n[i]+1)(λ_(i)+μ_(i))−Σ_(m∈Ω) _(j) _(\{j})ω_(im)} for j∈Ω _(n[i]−1) \{b[i]−1,n[i]},  (12)

ω_(i(n[i]))=Θ−2(n[i]+1)(λ_(i)+μ_(i))−Σ_(m=0) ^(n[i]−1)ω_(im)  (13)

and

τ_(i0)=Θ−φ_(i0).  (14)

If ∀i∈N_([ƒ,l−1]), ω_(i(b[i]−1))≧0, the conditions given in Theorem 1 are satisfied. That is, given Θ≧Π_(h), a one-wafer cyclic schedule is found for CTC[ƒ,l] by setting the ω_(ij)'s. However, i∈N_([ƒ,l−1]), ω_(i(b[i]−1))<0 may happen. Since Θ−2(n[i]+1)(λ_(i)+μ_(i))≧0 always holds. ω_(i(b[i]−1))<0 implies that τ_((i+1)0)−(4λ_(i)+3μ_(i))<0, leading to τ_((i+1)0)<(4λ_(i)+3μ_(i)), i.e., the conditions in Theorem 1 are violated. Thus, the above procedure can be used to not only schedule a CTC if a feasible one-wafer schedule exists for the given cycle time Θ, it also can be used to check the existence of a one-wafer cyclic schedule for CTC[ƒ,l] and this procedure is called CTC-Check.

Note that, in the above procedure, for i∈N_([ƒ,l−1]), ψ_(i2) is assigned ω_(ij)'s, j∈Ω_(n[i]−1)} as much as possible. In this way, ω_(i(n[i])) and φ_(i0)=4λ_(i)+3μ_(i)+ω_(i(n[i])) are minimized.

C.2. Minimal Cycle Time of a CTC

Given Θ≧Π_(h) for CTC[ƒ,l], if ω_(i(b[i]−1))<0, i∈N_([ƒ,l−1]), happens by using CTC-Check, one cannot find a one-wafer schedule with cycle time Θ. However, a one-wafer cyclic schedule still exists and such a schedule can be found by increasing Θ. The key is how to determine the minimal cycle time Θ and this issue is discussed as follows.

For CTC[ƒ,l] and given Θ, τ_((k+1)0)<(4λ_(k)+3μ_(k)), k∈N_([ƒ,l−1]), we assume that τ_((i+1)0)≧(4λ_(i)+3μ_(i)), i∈N_([k+1,l−1]), that is, the conditions given in Theorem 1 are satisfied for C_(i), ∀i∈N_([k+1,l−1]), but not satisfied for C_(k). Since 4λ_(k)+3μ_(k) is a constant, to make τ_((k+1)0)≧(4λ_(k)+3μ_(k)) satisfied, the only way is to increase τ_((k+1)0). Since τ_((k+1)0)=Θ−(4λ_(k+1)+3μ_(k+1)+ω_((k+1)(n[k+1]))) and (4λ_(k+1)+3μ_(k+1)) is a constant, to increase τ_((k+1)0), one needs to increase Θ and reduce ω_((k+1)(n[k+1])). By examining the CTC-Check procedure, one can find that, by increasing Θ, one can assign more part of ψ_((k+1)2) into ω_((k+1)j), j∈Ω_(n[k+1]−1)\{n[k+1]}, such that ω_((k+1)(n[k+1])) is reduced. In this way, τ_((k+1)0) can be increased by increasing Θ and reducing ω_((k+1)(n[k+1])) simultaneously.

By increasing Θ, to obtain a one-wafer cyclic schedule, the cycle time for any C_(i) in a K-tree-cluster tool should be increased as well such that every tool has the same cycle time. Hence, by increasing Θ for C_(i), i∈N_([k+2,l]), ω_(i(n[i])) is reduced. Then, by the CTC-Check procedure, this leads to the decrease of ω_((i−1)(n[i−1])). Finally, the increase of Θ for C_(i), i∈N_([k+2,l−1]), can result in the decrease of ω_((k+1)(n[k+1])). This implies that, with the increase of Θ, ω_((k+1)(n[k+1])) can be reduced by scheduling C_(k+1) and its downstream tools. However, given Θ, if CTC[ƒ,l] is scheduled by the CTC-Check procedure such that ω_(i(n[i]))=0, i∈N_([k+2,l]), the increase of Θ for C_(i) and its downstream tools has no effect on ω_((k+1)(n[k+1])), since ω_(i(n[i])) cannot be reduced any more. Thus, the effect of scheduling downstream tools on ω_((k+1)(n[k+1])) should be taken into account in finding the minimal cycle time.

Note that C_(l) is either a leaf or a fork. If it is a fork, it has multiple outgoing BMs. This means that CTC[θ, l] has multiple downstream CTCs. As above discussed, with the increase of Θ, scheduling tool C_(i) in a downstream CTC of CTC[θ, l] can result in the reduction of ω_((k+1)(n[k+1])). Also, given Θ, if a schedule for tool C_(i) in a downstream CTC is obtained by the CTC-Check procedure such that ω_(i(n[i]))=0, then, with the increase of Θ, scheduling C_(i) and its downstream tools has no effect on ω_((k+1)(n[k+1])).

With the above discussion, we present how to find the minimal cycle time and schedule the system as follows. Given Θ, for CTC[ƒ,l], every time if ω_(k(b[k]−1))<0, k∈N_([ƒ,l−1]) is returned by the CTC-Check procedure, the cycle time is increased by Δ, i.e. Θ←Θ+Δ, such that τ_((k+1)0)=(4λ_(k)+3μ_(k)). The key is how to determine Δ. To do so, define D[i]={PM_(ij)|j∈Ω_(n[i]−1)\{b[i]−1, n[i]}} be the set of steps in C_(i) and B[i]=|D[i]|, and B*[i]=B[i] if i≠l, and B*[l]=n[l]−1.

Given Θ, for CTC[ƒ,l], when ω_(k(b[k]−1))<0, k∈N_([ƒ,l−1]), is returned by the CTC-Check procedure, for every C_(i), i∈N_([k,l]), or in the downstream CTCs of CTC[ƒ,l], the robot waiting time is set by the CTC-Check procedure. To find Δ, for C_(i), i∈N_([k,l]), or in the downstream CTCs of CTC[ƒ,l], let A_(ij)=ω_(ij) and Λ_(ij)=τ_(ij), j∈Ω_(n[i]), and Φ=4λ_(k)+3μ_(k)−Λ_((k+1)0)=4λ_(k)+3μ_(k)−(Θ−(4λ_(k+1)+3μ_(k+1)+ω_((k+1)(n[k+1]))))=4λ_(k)+3μ_(k)+4λ_(k+1)+3μ_(k+1)+ω_((k+1)(n[k+1]))−Θ.

To take the effect of tools in the downstream CTCs of CTC[ƒ,l] into account, we use DsI(C_(i)), i∉Ψ, to denote the index set of all the downstream tools of C_(i) and LeafDsI(C_(i)), the index set of leaf tools in DsI(C_(i)). Further, define E(k)={g∈∪χ_((k,m))|m∈LeafDsI(C_(k)), ω_(j(n[j]))>0, j∈∪χ_((h,g)) and h=DI(C_(k)), and ω_((g+1)(n[g+1]))=0, g∉LeafDsI(C_(k))}. Further, let Y(k)={∪χ_((h,s))|s∈E(k) and h=DI(C_(k))}. Then, Δ is determined as follows. For g∈Y(k), let δ_(g)=ω_(g(n[g]))\Σ_(j∈Y(g))B*[j], δ_(k)=Φ\(1+Σ_(j∈Y(k))B*[j]), and Q(k)={a∈∪χ_((k,d))|d∈E(k)}. Then, let Δ=min_(h∈Q(k))δ_(h) and there are two cases.

Case 1: If Δ=δ_(k), let Θ←Θ+Δ. For ∀i∈DsI(C_(a)) and ∀a∈E(k), set ω_(i0)=A_(i0)+Δ, τ_(i0)=Λ_(i0)+Δ, and τ_(i(m−1))=Λ_(i(m−1))+Δ with m∈BI(C_(i)), when C_(i) is not a leaf tool. For ∀i∈Y(k)\E(k), ω_(ij)=A_(ij)+Δ, j∈Ω_(n[i])\{{j−1|j∈BI(C)}∪{n[i]}}, ω_(i(b[m,d]−1))=A_(i(b[m,d]−1))+Δ×Σ_(j∈{∪χ) _((d,a)) _(|a∈E(i)})B*[j]+Δ, m∈BI(C_(i)) and d=DI(C_(i)); ω_(i(n[i]))=A_(i(n[i]))−Δ×Σ_(j∈{═χ) _((i,a)) _(|a∈E(i)})B*[j]; and τ_(i0)=Λ_(i0)+Δ+Δ×Σ_(j∈{∪χ) _((i,a)) _(a∈E(i)})B*[j]. Then, with the increased Θ and the reset ω_(ij)'s, it is easy to verify that Conditions (9)-(10) are satisfied for i∈DsI(C_(k)).

Case 2: If Δ≠δ_(k), let Δ=δ_(g)=min_(h∈Q(k))δ_(h), Θ←Θ+Δ. Then, for ∀s∈E(k) and ∀i∈DsI(C_(s)), ω_(i0)=A_(i0)+Δ, τ_(i0)=Λ_(i0)+Δ, and τ_(i(m−1))=Λ_(i(m−1))+Δ, m∈BI(C_(i)), when C_(i) is not a leaf tool. For ∀i∈Y(k), ω_(ij)=A_(ij)+Δ, j∈Ω_(n[i])\{{j−1|j∈BI(C_(i))}∪{n[i]}}, ω_(i(m−1))=A_(i(m−1))+Δ×Σ_(j∈∪Y(i))B*[j]+Δ, m∈BI(C_(i)); ω_(i(n[i]))=A_(i(n[i]))−Δ×Σ_(j∈{∪χ) _((i,a)) _(|a∈E(i)})B*[j]; and τ_(i0)=Λ_(i0)+Δ+Δ×Σ_(j∈{∪χ) _((i,a)) _(|a∈E(i)})B*[j]. By doing so, it follows from (5)-(6) that Conditions (9)-(10) are satisfied for i∈DsI(C_(k)). Due to δ_(k)>Δ=δ_(g), we may have τ_((k+1)0)<(4λ_(k)+3μ_(k)) and A_(g(n[g]))=0, g∈∪χ_((k+1,a)), a∈E(k). In this case, let Θ←Θ+Δ. Then, repeat the above procedure for χ_((k+1,g−1)) until Case 1 occurs such that Δ=δ_(k) or A_((k+1)(n[k+1]))=0.

Finally the cycle time is increased by Δ such that τ_((k+1)0)=(4λ_(k)+3μ_(k)) and Conditions (9)-(10) for any pair of C_(i) and C_(i+1), i∈N_([ƒ,l]), are satisfied. In addition, since τ_((k+1)0)=(4λ_(k)+3μ_(k)), a cycle time increase that is less than Δ must result in τ_((k+1)0)<(4λ_(k)+3μ_(k)). For a CTC without downstream CTCs, a method is presented to find the minimal cycle time and its optimal one-wafer cyclic schedule in [Zhu et al., 2013b]. Since such method is somehow similar to the one disclosed herein, we do not go into too much detail and please refer to [Zhu et al., 2013b] for detail.

In the above development, each individual tool is scheduled such that, for any C_(i), ω_(i(n[i])) is minimized. Thus, it follows from Θ₁=Θ₂= . . . =Θ_(K)=Θ that φ_(i0), i∈N_([ƒ,l]), in CTC[ƒ,l] is minimized. The above development is summarized as Algorithm 1, where inputs φ and Θ≧Π_(h) represent the index set of the individual cluster tools in a CTC and the given cycle time, respectively. Assume that all α_(ij), λ_(i), μ_(i) of the K-cluster tool are known constants to Algorithm 1. Its output Θ is the CTC's minimal cycle time.

Algorithm 1: Scheduling CTC[f, l] for given Θ denoted as SCH(CTC[f, l], Θ) Input:Θ, N_([f, l]). Output: ω_(ij) for C_(i), i∈ N_([f, l]) and in the downstream CTCs, j∈Ω_(n[i]), φ_(f0), Θ, ε. 1. Initialization: 1.1. Ñ← l−f+1, AD ← Υ ← UI(f) 2. Set ω_(ij), j∈Ω_(n[l]) for R_(l) as: 2.1. For j← 0 to n[l] − 1 do 2.2. ω_(lj)← min{Θ− (4λ_(l) +3μ_(l)+α_(l(j+1))),Θ− 2(n[l] + 1)(λ_(l) + μ_(l)) − Σ_(m=0) ^(j−1) ω_(lm)} 2.3. EndFor 2.4. ω_(l(n[l])) ← Θ − 2(n[l] + 1)(λ_(l) + μ_(l)) − Σ_(m=0) ^(n[l]−1) ω_(lm) 2.5. τ_(l0) ← Θ − (4λ_(l)+ 3μ_(l)+ ω_(l(n[l]))), i←l − 1, a←Ñ− 1 3. If AD = 1 Then update ω_(mj)(m∈ DsI(C_(i))) as 3.1. For each j∈E₀ 3.2. ω_(j0) ←ω_(j0) + Δ, τ_(j0)←τ_(j0) + Δ 3.3. τ_(j(m−1))←τ_(j(m−1)) + Δ, for m∈BI(C_(j)) 3.4. EndFor 3.5. If E(i)≠Ø Then 3.6. For each j∈∪χ_((i+1, g))|g∈E(i) do 3.7. ω_(jm)←ω_(jm) + Δ, for m∈Ω_(n[j]) \ ({i−1|j∈BI(C_(i))}∪{n[j]}) 3.8. β← Σ_(m∈Y(j)) B*[m] 3.9. ω_(j(m−1)) ←ω_(j(m−1))+ Δ×β+ Δ, for m∈BI(C_(j)) 3.10. ω_(j(n[j])) ←ω_(j(n[j])) − Δ×β 3.11. τ_(j0) ←Σ_(j0)+Δ + Δ×β 3.12. EndFor 3.13. EndIf 3.14. a← |χ_((f,i))| 4. Set ω_(ij) for R_(i) (i∈χ_((f, l−i)), j∈Ω_(n[i])) as: 4.1. While a≧1 4.2. Ifτ_((i+1)0)< (4λ_(i) + 3μ_(i)) Then 4.3. Φ←4λ_(i) + 3μ_(i) − τ_((i+1)0) 4.4. If ω_((i+1)(n[i+1]))= 0 Then Δ ← Φ, E(i)←Ø, E₀←DsI(C_(i)) 4.5. Else 4.6. Find E(i) 4.7. δ_(k) ← ω_(k(n[k]))/ Σ_(m∈Y(k)) B*[m], for each k∈Y(i) 4.8. δ_(i)←Φ/(1 + Σ_(m∈Y(i)) B*[m]) 4.9. Δ←min{δ_(i), δ_(k) }with k∈Y(i) 4.10. E₀←∪DsI(C_(g))| g∈E(i) 4.11. EndIf 4.12. Θ←Θ+ Δ 4.13. AD ← 1 4.14. Goto 3. 4.15. EndIf 4.16. ω_(i(b[i]−1))← min{τ_((i+1)0)− (4λ_(i) + 3μ_(i)),Θ −2(n[i] + 1)(λ_(i) +μ_(i))} 4.17. For j← 0 to n[i] and j≠b[i] −1 do 4.18. ω_(ij)← min{Θ− (4λ_(i) +3μ_(i)+α_(i(j+1))),Θ− 2(n[i] + 1)(λ_(i) + μ_(i)) − Σ_(m∈Ω) _(j) _(\{j} ω) _(im) } 4.19. EndFor 4.20. τ_(i0)←Θ− (4λ_(i)+ 3μ_(i) + ω_(i(n[i]))), i← i− 1, a ←a− 1 4.21. EndWhile. 5. Set φ_(f0) and Θ_(i) as: 5.1. If Υ≠ 0 and 4λ_(r) + 3μ_(r) >τ_(f0) Then 5.2. ←4λ_(r) + 3μ_(r) −τ_(f0) 5.3. If ω_(f(n[f]))= 0 Then Δ←Φ, E(i)←Ø, E₀←DsI(C_(i)) 5.4. Else 5.5. Find E(f) 5.6. δ_(k) ← ω_(k(n[k]))/ Σ_(m∈Y(k)) B*[m], for each k∈Y(f) 5.7. δ_(f)←Φ/(1+ Σ_(m∈Y(f)) B*[m]) 5.8. Δ←min{δ_(f), δ_(k) }with k∈Y(f) 5.9. E₀←∪DsI(C_(f))|f∈E(f) 5.10. EndIf 5.11. Θ←Θ+ Δ, Adjusting← 1 5.12. GoTo 3 5.13. EndIf 5.14. ε ← Θ −Θ₀ 5.15. φ_(f0) ← 4λ_(f)+ 3μ_(f) + ω_(f(n[f])) 6. End

C.3. Scheduling K-Tree-Cluster Tool

As above discussed, a K-tree-cluster tool is composed of a number of CTCs, to obtain the minimal cycle time of the K-tree-cluster tool, it needs to minimize φ_(i0)=4λ_(i)+3μ_(i)+ω_(i(n[i])) for every C_(i) in each CTC, or minimize ω_(i(n[i])). This is done as follows. Let L-CTC be the set of CTC[ƒ,l]s with C_(l) being a leaf tool of the K-tree-cluster tool and assume that |L-CTC|=g. Given Θ=Π_(h), the lower bound of cycle time for a K-tree-cluster tool, as the initial cycle time, each CTC in the L-CTC is scheduled by using Algorithm 1. Assume that the minimal cycle time obtained for these g CTCs are Θ₁, . . . , Θ_(g), respectively. Then, given Θ=max{Θ₁, . . . , Θ_(g)} as the cycle time, for any C_(i) in any CTC that is in L-CTC, ω_(ij)'s are set by the CTC-Check procedure. After that, given Θ=max{Θ₁, . . . , Θ_(g)} as initial cycle time, we schedule the CTC∉L-CTC that is not scheduled and whose downstream CTCs are all in L-CTC. By repeating such a process, one can find a one-wafer cyclic schedule for the entire K-tree-cluster tool by minimizing φ_(i0)=4λ_(i)+3μ_(i)+ω_(i(n[i])) for every individual tool C_(i). If finally the obtained minimal cycle time is Θ=Π_(h), the lower bound of cycle time is reached by a one-wafer cyclic schedule. We present Algorithm 2 as follows to implement above sketchy procedure.

Algorithm 2: Schedule a K-tree-cluster tool. Input: α_(ij), λ_(i), μ_(i) (i ∈ N_(K),j∈Ω_(n[i])) Output: ω_(ij), Θ 1. Initialization: Π_(h)← max(Π_(i)), i ∈ N_(K), Ψ ←Ø, Ψ₁ ←Ø 2. For each i∈ N_(K) do 3. If (∀j∈N_(n[i]), α_(ij)>0) Then Ψ ← Ψ∪{i} EndIf 4. EndFor 5. While Ψ ≠ Ø 6. For each e∈Ψ do 7.  

 ← {e}, m←UI(C_(e)) 8. While m ≠ 0 and O-Deg(C_(m)) = 1 do 

 ← 

 ∪{m}, m←UI(C_(m))   EndWhile 9. f← min( 

 ) 10. i← UI(C_(f)), j← the step index of PM_(i(b[i,f])) in C_(i) 11. Ψ₁← Ψ₁∪ {i} 12. Π_(new)← max(Π_(h), max(Π_(m))|m∈ 

 ) 13. Call SCH(CTC[f, l], Π_(new)) to set φ_(f0), Θ, ε_(ij) 14. α_(ij)← φ_(f0) 15. EndFor 16. Ψ←Ø 17. For each i∈Ψ₁ do 18. If ∀j∈BI(i), α_(ij) > 0 Then 19. ε← max(ε_(ij)), j∈BI(i), Ψ₁←Ψ₁\{i}, and Ψ←Ψ∪ {i} 20. For ∀j∈BI(i) and ε_(ij)<ε 21. ω_(m0)←ω_(m0) + (ε−ε_(ij)), τ_(m0)←τ_(m0) + (ε−ε_(ij)), m∈DsI(C_(j)) 22. EndFor 23. EndIf 24. EndFor 25. EndWhile

By Algorithm 2, the CTCs in a K-tree-cluster tool are identified and they are scheduled by calling Algorithm 1 to find the minimal cycle time and set the robots' waiting time. This is done from the leaves to the root in a backward way as above discussed. Also, by Algorithm 2, if Θ=Π_(h) is returned, it implies that τ_((i+1)0)<(4λ_(i)+3μ_(i)), ∀i∈N_(K), does not occur and a one-wafer cyclic schedule with the lower bound of cycle time is obtained. Therefore, Algorithm 2 can be used to decide whether the K-tree-cluster tool can be scheduled to reach the lower bound of cycle time. Thus, we have Theorem 2 immediately.

Theorem 2: For a process-dominant K-tree-cluster tool, there is a one-wafer cyclic schedule such that its cycle time is the lower bound Π_(h) if and only if, by Algorithm 2, Θ=Π_(h) is returned.

Moreover, by the proposed method, if Θ>Π_(h) is returned, the obtained one-wafer cyclic schedule is optimal as stated by the following theorem.

Theorem 3: For a process-dominant K-tree-cluster tool, by Algorithm 2, if Θ>Π_(h) is returned, the obtained one-wafer cyclic schedule is optimal in terms of cycle time.

Proof. By applying Algorithm 2 to a K-tree-cluster tool, every time if τ_((i+1)0)<(4λ_(i)+3μ_(i)), i∈N_(K), occurs, the cycle time Θ is increased by Δ as Θ←Θ+Δ such that τ_((i+1)0)=(4λ_(i)+3μ_(i)). Hence, by Algorithm 2, finally a one-wafer cyclic schedule is obtained with cycle time Θ>Π_(h), this implies that there is at least an i∈N_(K) such that τ_((i+1)0)=(4λ_(i)+3μ_(i)). In this case, any decrease of Θ would result in τ_((i+1)0)=(4λ_(i)+3μ_(i)), leading to the violation of Conditions (9)-(10). Thus, Θ>Π_(h) obtained is the minimal cycle time.

Note that, for a process-dominant K-tree-cluster tool addressed in this work, each individual tool is scheduled by using a backward scheduling strategy. This implies that a backward scheduling strategy is optimal for such a K-tree-cluster tool if the proposed method is applied.

D. ILLUSTRATIVE EXAMPLES

In this section, we use two examples to show how to apply the proposed method and its effectiveness. In the examples, the time is in seconds and is omitted for simplicity.

Example 1

It is from [Chan et al., 2011b]. As shown in FIG. 4, it is a 3-tree-cluster tool composed of three single-arm cluster tools, where PS₁₀ is the LLs, PS₂₀ and PS₃₀ are incoming buffers for C₂ and C₃, and, PS₁₂ and PS₁₃ are outgoing buffers for C₁. The activity time is as follows: for C₁, (α₁₀, α₁₁, α₁₂, α₁₃, α₁₄; λ₁, μ₁)=(0, 10, 0, 0, 10; 5, 2); for C₂, (α₂₀, α₂₁, α₂₂, α₂₃, α₂₄; λ₂, μ₂)=(0, 70, 70, 58, 5; 3, 1); and for C₃, (α₃₀, α₃₁, α₃₂, α₃₃, α₃₄; λ₃, μ₃)=(0, 5, 58, 70, 70; 3, 1). The wafer processing route is LL→PS₁₁→PS₁₂ (PS₂₀)→PS₂₁→PS₂₂→PS₂₃→PS₂₄→PS₂₀ (PS₁₂)→PS₁₃ (PS₃₀)→PS₃₁→PS₃₂→PS₃₃→PS₃₄→PS₃₀ (PS₁₃)→PS₁₄→LL. It contains three CTCs: CTC[2, 2], CTC[3, 3], and CTC[1, 1]. For this example, a multi-wafer cyclic schedule is obtained with cycle time 102.8 in [Chan et al., 2011b].

By η_(ij)=α_(ij)+4λ_(i)+3μ_(i), ψ_(i1)=2(n[i]+1)(λ_(i)+μ_(i)), and Π_(i)=max{η_(i0), η_(i1), . . . , η_(i(n[i])), ψ_(i1)}, for C₁, η₁₀=α₁₀+4λ₁+3μ₁=0+4×5+3×2=26, η₁₁=36, η₁₂=26, η₁₃=26, η₁₄=36, and ψ₁₁=2(n[1]+1)(μ₁+λ₁)=2×(4+1)×(5+2)=70. We have Π₁=70 and C₁ is transport-bound. For C₂, we have that η₂₀=15, η₂₁=85, η₂₂=85, η₂₃=73, η₂₄=20, and ψ₂₁=40. Thus we have Π₂=85 and C₂ is process-bound. For C₃, we have that η₃₀=15, η₃₁=20, η₃₂=73, η₃₃=85, η₃₄=85, and ψ₃₁=40. Then we have Π₃=85 and C₃ is process-bound. Since Π₂=Π₃>Π₁, and C₂ and C₃ are process-bound, the 3-tree-cluster is process-dominant.

With the lower bound of cycle time being Π_(h)=Π₂=Π₃=85, set Θ=Π_(h)=85 as initial cycle time, we have ψ₁₂=Θ−ω₁₁=15, ψ₂₂=Θ−ψ₂₁=40, and ψ₃₂=Θ−ψ₂₁=40. By Algorithm 2 that calls Algorithm 1, ω_(ij)'s are set as ω₂₀=ω₂₁=0, ω₂₂=12, ω₂₃=33, ω₂₄=0, ω₃₀=45, ω₃₁=ω₃₂=ω₃₃=ω₃₄=0, ω₁₀=ω₁₁=ω₁₂=ω₁₃=0 and ω₁₄=15 such that Θ=Π_(h)=85 is returned. Hence, a one-wafer cyclic schedule with the lower bound of cycle time Θ=85 is obtained, which verifies Theorem 2.

With this topology of this example, by changing the activity time, four other cases are generated and studied in [Chan et al., 2011b] by using the method proposed there. For each of these cases, a multi-wafer cyclic schedule is obtained. These cases are also studied by the proposed method and a one-wafer cyclic schedule is obtained for each of the cases with the cycle time being less than that obtained in [Chan et al., 2011b].

Example 2

It is a 5-tree-cluster tool composed of five single-arm cluster tools as shown in FIG. 1, where PS₁₀ is the LLs, PS₂₀, PS₃₀, PS₄₀, and PS₅₀ are incoming buffers for C₂, C₃, C₄, and C₅, PS₁₂ and PS₁₃ are outgoing buffers for C₁, and PS₃₂ and PS₄₂ are outgoing buffers for C₃ and C₄. The activity time is as: for C₁, (α₁₀, α₁₁, α₁₂, α₁₃, α₁₄; λ₁, μ₁)=(0, 10, 0, 0, 10; 5, 2); for C₂, (α₂₀, α₂₁, α₂₂, α₂₃, α₂₄; λ₂, μ₂)=(0, 70, 70, 58, 5; 3, 1); for C₃, (α₃₀, α₃₁, α₃₂, α₃₃, α₃₄; λ₃, μ₃)=(0, 34, 0, 31, 4; 8, 1); for C₄, (α₄₀, α₄₁, α₄₂, α₄₃, α₄₄; λ₄, μ₄)=(0, 100, 0, 100, 98; 1, 1); and for C₅, (α₅₀, α₅₁, α₅₂, α₅₃, α₅₄; λ₅, μ₅)=(0, 100, 100, 100, 100; 1, 1). The wafer processing route is given in Section II. It contains three CTCs: CTC[3, 5], CTC[2, 2], and CTC[1, 1].

By η_(ij)=α_(ij)+4λ_(i)+3μ_(i), ψ_(i1)=2(n[i]+1)(λ_(i)+μ_(i)), and Π_(i)=max{η_(i0), η_(i1), . . . , η_(i(n[i])), ψ_(i1)}, for C₁, η₁₀=α₁₀+4λ₁+3μ₁=0+4×5+3×2=26, η₁₁=36, η₁₂=26, η₁₃=26, η₁₄=36, and ψ₁₁=70. We have Π₁=70 and C₁ is transport-bound. For C₂, we have that η₂₀=15, η₂₁=85, η₂₂=85, η₂₃=73, η₂₄=20, and ψ₂₁=40. Then we have Π₂=85 and C₂ is process-bound. For C₃, we have that η₃₀=35, η₃₁=69s, η₃₂=35, η₃₃=66, η₃₄=39, and ψ₃₁=90. Thus we have Π₃=90 and C₃ is transport-bound. For C₄, we get η₄₀=7s, η₄₁=107, η₄₂=7s, η₄₃=107, η₄₄=105s, and ψ₄₁=20. Therefore, we have Π₄=107 and C₄ is process-bound. For C₅, it is found that η₅₀=7s, η₅₁=107, η₅₂=107, η₅₃=107, η₅₄=107, and ψ₅₁=20. Hence we have Π₅=107 and C₅ is process-bound. Since Π₄=Π₅≧Π_(i), i∈N₃, and C₄ and C₅ are process-bound, the 5-tree-cluster is process-dominant.

With the lower bound of cycle time being Π₄=Π₅=107, set Θ=107 as initial cycle time. By ψ_(i2)=Θ−ψ_(i1), we have ψ₁₂=37, ψ₂₂=67, ψ₃₂=17, ψ₄₂=87, and ψ₅₂=87. By applying Algorithm 2 to CTC[3, 5], the cycle time is increased as Θ←Θ+Δ←Θ+((4λ₃+3μ₃)−τ₄₀)/(1+B*[4]+B*[5])=107+(35−21)/(1+3+3)=107+2=109. Then, Θ=109 is set for scheduling CTC[2, 2] and CTC[1, 1]. Then, given Θ=109, by applying Algorithm 2 to CTC[2, 2] and CTC[1, 1] sequentially, the cycle time Θ=109 is returned and ω_(ij)'s are set as: ω₃₀=19, ω₃₁=ω₃₂=ω₃₃=ω₃₄=0, ω₄₀=2, ω₄₁=14, ω₄₂=2, ω₄₃=4, ω₄₄=67, ω₅₀=ω₅₁=ω₅₂=ω₅₃=2, ω₅₄=81, ω₁₀=39, ω₁₁=ω₁₂=ω₁₃=ω₁₄=0, ω₂₀=24, ω₂₁=22, ω₂₂=23, ω₂₃=0, and ω₂₄=0. Hence, an optimal one-wafer cyclic schedule with cycle time Θ=109 is obtained.

E. THE PRESENT INVENTION

The present invention is developed based on the theoretical development in Sections A-C above.

An aspect of the present invention is to provide a computer-implemented method for scheduling a tree-like multi-cluster tool to generate a one-wafer cyclic schedule. The multi-cluster tool comprises K single-cluster tools C_(i)'s. The multi-cluster tool is decomposable into a plurality of CTCs each having a serial topology.

Exemplarily, the method makes use of the finding given by Theorem 1. In particular, the method comprises determining values of ω_(ij), i=1, 2, . . . , K, and j=0, 1, . . . , n[i], such that (8)-(10) are satisfied for any pair of C_(i) and C_(a), in which i, a∈N_(K), C_(a) being an immediate downstream tool of C_(i).

In determining the aforesaid values of ω_(ij), preferably a CTC-Check algorithm for computing candidate values of ω_(ij) under a constraint on Θ. The computation of the candidate ω_(ij) values is performed for at least one individual CTC. The CTC-Check algorithm for CTC[ƒ,l] is configured to compute the candidate ω_(ij) values such that ω_(i(n[i])) is minimized for each value of i selected from N_([ƒ,l−1]). In one embodiment, the CTC-Check algorithm as used in the disclosed method is the one given by Section C.1.

Based on the CTC-Check algorithm, the values of ω_(ij), i=1, 2, . . . , K, and j=0, 1, . . . , n[i], are determined as follows according to one embodiment of the present invention. First, the candidate values of ω_(ij) for the single-cluster tools in an individual CTC are determined by performing the CTC-Check algorithm under the constraint that Θ is equal to a lower bound of cycle time of the multi-cluster tool. This lower bound is a FP of a bottleneck tool among the K single-cluster tools, and is given by Π_(h). If the candidate ω_(ij) values for the individual CTC are non-negative, then the candidate ω_(ij) values are set as the determined values of ω_(ij) for the individual CTC. If at least one of the candidate ω_(ij) values for the individual CTC is negative, a minimum cycle time for the individual CTC is first determined and then the values of ω_(ij) for the individual CTC are determined by performing the CTC-Check algorithm under a revised constraint that Θ is the determined minimum cycle time for the individual CTC.

More particularly, it is preferable and advantageous that the values of ω_(ij), i=1, 2, . . . , K, and j=0, 1, . . . , n[i], are determined according to the procedure given in Section C.3, where the procedure is embodied in Algorithms 1 and 2 detailed above. A summary of this procedure is given as follows. First identify an L-CTC set consisting of first individual CTCs each having a leaf tool therein. It follows that one or more second individual CTCs not in the L-CTC set are also identified. For each of the first individual CTCs, a minimum cycle time is determined, followed by determining the values of ω_(ij) by performing the CTC-Check algorithm under the constraint that Θ is equal to the determined minimum cycle time. For each second individual CTC, candidate values of ω_(ij) for each single-cluster tool therein are determined by performing the CTC-Check algorithm under an initial constraint that Θ is equal to a maximum value of the minimum cycle times already determined for the first individual CTCs. If the candidate ω_(ij) values for the second individual CTC are non-negative, then the candidate ω_(ij) values are set as the determined values of ω_(ij) for the second individual CTC. If, on the other hand, at least one of the candidate ω_(ij) values for the second individual CTC is negative, then the following two-step procedure is performed. First, determine a minimum cycle time for the second individual CTC. Second, determine the values of ω_(ij) for the second individual CTC by performing the CTC-Check algorithm under a revised constraint that Θ is equal to the determined minimum cycle time for the second individual CTC.

The embodiments disclosed herein may be implemented using general purpose or specialized computing devices, computer processors, or electronic circuitries including but not limited to digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA), and other programmable logic devices configured or programmed according to the teachings of the present disclosure. Computer instructions or software codes running in the general purpose or specialized computing devices, computer processors, or programmable logic devices can readily be prepared by practitioners skilled in the software or electronic art based on the teachings of the present disclosure.

In particular, the method disclosed herein can be implemented in a tree-like multi-cluster cluster tool if the multi-cluster tool includes one or more processors. The one or more processors are configured to execute a process of generating a one-wafer cyclic schedule according to one of the embodiments of the disclosed method.

The present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiment is therefore to be considered in all respects as illustrative and not restrictive. The scope of the invention is indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. 

What is claimed is:
 1. A computer-implemented method for scheduling a tree-like multi-cluster tool to generate a one-wafer cyclic schedule, the multi-cluster tool comprising K single-cluster tools denoted as C_(i), i=1, 2, . . . , K, the single-cluster tool C_(i) having a robot R_(i) for wafer handling, the multi-cluster tool being decomposable into a plurality of cluster-tool-chains (CTCs) each having a serial topology, an individual CTC consisting of single-cluster tools C_(i), C_(i+1), . . . C_(i′) being denoted as CTC[i,i′], the method comprising: determining values of ω_(ij), i=1, 2, . . . , K, and j=0, 1, . . . , n[i], such that: θ_(lm)=θ_(aƒ) =Θ, m∈Ω _(n[l]) and ƒ∈Ω_(n[a]); τ_(l(b[l,a]))≧4λ_(a)+3μ_(a)+ω_(a(n[a])); and τ_(a0)≧4λ_(l)+3μ_(l)+ω_(l(b[l,a]−1));  are satisfied for any pair of C_(l) and C_(a), in which l, a∈N_(K), C_(a) being an immediate downstream tool of C_(l); wherein: processing steps performed by C_(i) are indexed from 0 to n[i], an initial processing step of C_(i) having an index 0 and being a step of loading a wafer from a loadlock or a buffer module of C_(i) to R_(i); b[l,a] is an index denoting a step of putting a wafer in C_(l) to a buffer module shared by C_(l) and C_(a); ω_(ij) is a robot waiting time for R_(i) to wait at an m-th step in C_(i) before unloading a wafer held by R_(i) to a next step; θ_(ij)=τ_(ij)+4λ_(i)+3μ_(i)+ω_(i(j−1)) , i∈N _(K) and j∈N _(n[i]); θ_(i0)=τ_(i0)+4λ_(i)+3μ_(i)+ω_(i(n[i])) , i∈N _(K); Θ is a cycle time of the multi-cluster tool; τ_(ij) is a wafer sojourn time at the j-th step in C_(i); λ_(i) is a time taken by R_(i) to load or unload a wafer in C_(i); μ_(i) is a time taken by R_(i) to move between process modules of C_(i) for transiting from one processing step to another; N_(L)={1, 2, . . . , L} for a positive integer L; and Ω_(L)=N_(L)∪{0}.
 2. The method of claim 1, wherein the determining of the values of ω_(ij), i=1, 2, . . . , K, and j=0, 1, . . . , n[i] comprises performing a CTC-Check algorithm to compute candidate values of ω_(ij) under a constraint on Θ for at least one individual CTC, wherein the CTC-Check algorithm for CTC[ƒ,l] is configured to compute the candidate ω_(ij) values such that ω_(i(n[i])) is minimized for each value of i selected from N_([ƒ,l−1]).
 3. The method of claim 2, wherein the CTC-Check algorithm for CTC[ƒ,l] comprises the steps of: setting ω_(ij)=min{Θ−(4λ_(l)+3μ_(l)+α_(l(j+1))), Θ−2(n[l]+1)(λ_(l)+μ_(l))−Σ_(m∈Ω) _(j) _(\{j})ω_(lm)}, j∈Ω_(n[l]−1); setting ω_(l(n[l]))=Θ−2(n[l]+1)(λ_(l)+μ_(l))−Σ_(m=0) ^(n[l]−1)ω_(lm); setting τ_(l0)=Θ−φ_(l0); and setting values of ω_(ij)'s i=l−1 to ƒ according to: ω_(i(b[i]−1))=min{τ_((i+1)0)−(4λ_(i)+3μ_(i)),Θ−2(n[i]+1)(λ_(i)+μ_(i))}; ω_(ij)=min{Θ−(4λ_(i)+3μ₁+α_(i(j+1))),Θ₀−2(n[i]+1)(λ_(i)+μ_(i))−Σ_(m∈Ω) _(j) _(\{j})ω_(im)} for j∈Ω _(n[i]−1) \{b[i]−1,n[i]}; ω_(i(n[i]))=Θ−2(n[i]+1)(λ_(i)+μ_(i))−Σ_(m=0) ^(n[i]−1)ω_(im); and τ_(i0)=Θ−φ_(i0).
 4. The method of claim 3, wherein the determining of the values of ω_(ij), i=1, 2, . . . , K, and j=0, 1, . . . , n[i], further comprises: determining the candidate values of ω_(ij) for the single-cluster tools in an individual CTC by performing the CTC-Check algorithm under the constraint that Θ is equal to a lower bound of cycle time of the multi-cluster tool, the lower bound of cycle time of the multi-cluster tool being a fundamental period of a bottleneck tool among the K single-cluster tools; if the candidate ω_(ij) values for the individual CTC are non-negative, then setting the candidate ω_(ij) values as the determined values of ω_(ij) for the individual CTC; and if at least one of the candidate ω_(ij) values for the individual CTC is negative, then: determining a minimum cycle time for the individual CTC; and determining the values of ω_(ij) for the individual CTC by performing the CTC-Check algorithm under a revised constraint that Θ is the determined minimum cycle time for the individual CTC.
 5. The method of claim 3, wherein the determining of the values of ω_(ij), i=1, 2, . . . , K, and j=0, 1, . . . , n[i], further comprises: identifying an L-CTC set consisting of first individual CTCs each having a leaf tool therein, whereby one or more second individual CTCs not in the L-CTC set are also identified; for each of the first individual CTCs, determining a minimum cycle time and then determining the values of ω_(ij) by performing the CTC-Check algorithm under the constraint that Θ is equal to the determined minimum cycle time; for each second individual CTC, determining candidate values of ω_(ij) for each single-cluster tool therein by performing the CTC-Check algorithm under an initial constraint that Θ is equal to a maximum value of the minimum cycle times already determined for the first individual CTCs; if the candidate ω_(ij) values for the second individual CTC are non-negative, then setting the candidate ω_(ij) values as the determined values of ω_(ij) for the second individual CTC; and if at least one of the candidate ω_(ij) values for the second individual CTC is negative, then: determining a minimum cycle time for the second individual CTC; and determining the values of ω_(ij) for the second individual CTC by performing the CTC-Check algorithm under a revised constraint that Θ is equal to the determined minimum cycle time for the second individual CTC.
 6. The method of claim 1, wherein the robots R_(i), i=1, 2, . . . , K, are single-arm.
 7. A tree-like multi-cluster tool comprising K single-cluster tools each having a robot for wafer handling, the multi-cluster tool being decomposable into a plurality of cluster-tool-chains each having a serial topology, wherein the multi-cluster tool further comprises one or more processors configured to execute a process of generating a one-wafer cyclic schedule according to the method of claim
 1. 8. A tree-like multi-cluster tool comprising K single-cluster tools each having a robot for wafer handling, the multi-cluster tool being decomposable into a plurality of cluster-tool-chains each having a serial topology, wherein the multi-cluster tool further comprises one or more processors configured to execute a process of generating a one-wafer cyclic schedule according to the method of claim
 2. 9. A tree-like multi-cluster tool comprising K single-cluster tools each having a robot for wafer handling, the multi-cluster tool being decomposable into a plurality of cluster-tool-chains each having a serial topology, wherein the multi-cluster tool further comprises one or more processors configured to execute a process of generating a one-wafer cyclic schedule according to the method of claim
 3. 10. A tree-like multi-cluster tool comprising K single-cluster tools each having a robot for wafer handling, the multi-cluster tool being decomposable into a plurality of cluster-tool-chains each having a serial topology, wherein the multi-cluster tool further comprises one or more processors configured to execute a process of generating a one-wafer cyclic schedule according to the method of claim
 4. 11. A tree-like multi-cluster tool comprising K single-cluster tools each having a robot for wafer handling, the multi-cluster tool being decomposable into a plurality of cluster-tool-chains each having a serial topology, wherein the multi-cluster tool further comprises one or more processors configured to execute a process of generating a one-wafer cyclic schedule according to the method of claim
 5. 12. A tree-like multi-cluster tool comprising K single-cluster tools each having a robot for wafer handling, the multi-cluster tool being decomposable into a plurality of cluster-tool-chains each having a serial topology, wherein the multi-cluster tool further comprises one or more processors configured to execute a process of generating a one-wafer cyclic schedule according to the method of claim
 6. 